A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

Automated summary

Fault detection and identification are crucial for ensuring the precision and reliability of INS/GNSS-integrated navigation systems. This paper introduces a method using the Variance Shift Outlier Model (VSOM) for fault detection in GNSS measurements, where outliers are identified based on variance shifts and testing statistics.